I am an associate professor in the School of Information at the University of Michigan School.
NSF CAREER Award Recipient
Google Faculty Award Recipient
Facebook Faculty Award Recipient
NSF Algorithms in the Field Grant Recipient
NSF CCF Small Recipient (3x)
Bo Li - former Post-Doc, now TT at UIUC
Yuqing Kong - former PhD student, now TT at Peking University
Fang-Yi Yu - former PhD Student/Post-Doc, now TT at George Mason University
Biaoshuai Tao - former PhD Student, now TT at SJTU
Noah Burrell - former PhD Student, now at Epistemix
Yichi Zhang - current PhD Student
Md Sanzeed Anwar - current PhD Student
Shengwei Xu - current PhD Student
Christian David Gamba Contrera - current PhD Student
Eliciting Informative Text Evaluations with Large Language
Models
Y. Lu, S. Xu, Y. Zhang, Y. Kong, G. Schoenebeck
PDF
Measurement Integrity in Peer Prediction: A Peer Assessment Case Study
N. Burrell, G. Schoenebeck
EC '23, Arxiv
High-Effort Crowds: Limited Liability via Tournaments
Y. Zhang, G. Schoenebeck
WWW'23
False Consensus, Information Theory, and Prediction Markets.
Y. Kong, G. Schoenebeck
ITCS '23 , arXiv
Two Strongly Truthful Mechanisms for Three Heterogeneous Agents Answering One Question
G. Schoenebeck, F. Yu
TEAC, 2023,
Wine 2020,
pdf
Wisdom of the Crowd Voting: Truthful Aggregation of Voter Information and Preferences
G. Schoenebeck, b. Tao
NeurIPs, 2021, Arxiv
Learning and Strongly Truthful Multi-Task Peer Prediction: A Variational Approach
G. Schoenebeck, F. Yu.
ITCS 2021, Arxiv.
An Information Theoretic Framework For Designing Information Elicitation Mechanisms That Reward Truth-telling
Y. Kong, G. Schoenebeck.
TEAC '19,
Arxiv
Eliciting Informative Text Evaluations with Large Language
Models
Y. Lu, S. Xu, Y. Zhang, Y. Kong, G. Schoenebeck
PDF
Spot Check Equivalence: an Interpretable Metric for Information Elicitation Mechanisms
S. Xu, Y. Zhang, P. Resnick, G. Schoenebeck
WWW '24, arXiv
Filter Bubble or Homogenization? {D}isentangling the Long-Term Effects of Recommendations on User Consumption Patterns
S. Anwar and G. Schoenebeck, P. Dhillon
WWW '24, arXiv
Exit Ripple Effects: Understanding the Disruption of Socialization Networks Following Employee Departures
D. Gamba, Y. Yu, Y. Yuan, G. Schoenebeck, D. Romero
WWW '24, arXiv
Eliciting Honest Information From Authors Using Sequential Review
Y. Zhang, G. Schoenebeck, W. Su
AAAI '24, arXiv
Testing Conventional Wisdom (of the Crowd)
N. Burrell, G. Schoenebeck
UAI'23
Measurement Integrity in Peer Prediction: A Peer Assessment Case Study
N. Burrell, G. Schoenebeck
EC '23, Arxiv
The Wisdom of Strategic Voting
Q. Han, G. Schoenebeck, B. Tao, L. Xia
EC '23, Arxiv
High-Effort Crowds: Limited Liability via Tournaments
Y. Zhang, G. Schoenebeck
WWW'23
Multitask Peer Prediction With Task-dependent Strategies
Y. Zhang, G. Schoenebeck
WWW'23
Two Strongly Truthful Mechanisms for Three Heterogeneous Agents Answering One Question
G. Schoenebeck, F. Yu
TEAC, 2023,
Wine 2020,
pdf
False Consensus, Information Theory, and Prediction Markets.
Y. Kong, G. Schoenebeck
ITCS '23 , arXiv
A System-Level Analysis of Conference Peer Review.
Y. Zhang, F. Yu, G. Schoenebeck, and D. Kempe
EC '22.
Optimal Local Bayesian Differential Privacy Over Markov Chains.
D. Chakrabarti, J. Gao, A. Saraf, G. Schoenebeck, and F. Yu
AAMAS '22 (extended abstract), arXiv.
Bayesian Persuasion in Sequential Trials.
S. Su, V. Subramanian, and G. Schoenebeck
WINE '21, arXiv.
Adaptive Greedy Versus Non-adaptive Greedy for Influence Maximization
W. Chen, B. Peng, G. Schoenebeck, B. Tao
JAIR '22, AAAI '20, arXiv
BONUS! Maximizing Surprise Labels
Z. Huang, Y. Kong, T. X. Liu, G. Schoenebeck, S. Xu
WWW '22, Arxiv
Wisdom of the Crowd Voting: Truthful Aggregation of Voter Information and Preferences
G. Schoenebeck, b. Tao
NeurIPs, 2021, Arxiv, 2021
SURPRISE! and When to Schedule It
Z. Huang, S. Xu, Y. Shan, Y. Lu, Y. Kong, T. X. Liu, G. Schoenebeck
IJCAI '21, Arxiv
Survey Equivalence: A Procedure for Measuring Classifier Accuracy Against Human Labels
P. Resnick, Y. Kong, G. Schoenebeck, T. Weninger
Arxiv, 2021
Information Elicitation from Rowdy Crowds
G. Schoenebeck, F. Yu, Y. Zhang
WWW '21
Timely Information from Prediction Markets
Learning and Strongly Truthful Multi-Task Peer Prediction: A Variational Approach
Relaxing Common Belief for Social Networks
Escaping Saddle Points in Constant Dimensional Spaces: An Agent-based Modeling Perspective
Limitations of greed: Influence maximization in undirected networks re-visited
Information Elicitation Mechanisms for Statistical Estimation
Influence Maximization on Undirected Graphs: Towards Closing the (1-1/e) Gap
Outsourcing computation: the minimal refereed mechanism.
Think globally, act locally: On the optimal seeding for nonsubmodular influence maximization.
An Information Theoretic Framework For Designing Information Elicitation Mechanisms That Reward Truth-telling
Complex Contagions in Charitable Donations
Beyond Worst-Case (In)approximability of Nonsubmodular Influence Maximization
Think Globally, Act Locally: On the Optimal Seeding for Nonsubmodular Influence Maximization
The Volatility of Weak Ties: Co-evolution of Selection and Influence
in Social Networks
Outsourcing Computation: the Minimal Refereed Mechanism
Social learning with questions
Water from Two Rocks: Maximizing the Mutual Information
Eliciting Expertise without Verification
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
Consensus of Interacting Particle Systems on Erdos-Renyi Graphs
Optimizing Bayesian Information Revelation Strategy in Prediction Markets: the Alice Bob Alice Case
Equilibrium Selection in Information Elicitation without Verification via Information Monotonicity
Contention-Aware Lock Scheduling for Transactional Databases
Don't Be Greedy: Leveraging Community Structure to Find High Quality Seed Sets for Influence Maximization
Cascades and Myopic Routing in Nonhomogeneous Kleinbergs Small World Model
A Top-Down Approach to Achieving Performance Predictability in Database Systems
Engineering Agreement:The Naming Game with Asymmetric and Heterogeneous Agents
How Complex Contagions Spread Quickly in Preferential Attachment Models and Other Time-Evolving Networks
Sybil Detection Using Latent Network Structure
General Threshold Model for Social Cascades: Analysis and Simulations
Complex Contagions on Configuration Model Graphs with a Power-Law Degree Distribution
Putting Peer Prediction Under the Micro(economic)scope and Making Truth-telling Focal
Identifying the Major Sources of Variance in Transaction Latencies: Towards More Predictable Databases
A Framework For Designing Information Elicitation Mechanisms That Reward Truth-telling
Complex Contagions in Kleinberg's Small World Model
Buying Private Data without Verification
Characterizing Strategic Cascades on Networks
Graph Isomorphism and the Lasserre Hierarchy
Better Approximation Algorithms for the Graph Diameter.
Potential Networks, Contagious Communities, and Social Network Structure.
Conducting Truthful Surveys, Cheaply
Finding Overlapping Communities in Social Networks: Toward a Rigorous Approach
Social Learning in a Changing World
General Hardness Amplification of Predicates and Puzzles
Constrained Non-monotone Submodular Maximization: Offline and Secretary Algoritms.
The Limitations of Linear and Semidefinite Programs
Optimal Testing of Reed-Muller Codes
Detecting Spam in a Twitter Network.
Reaching Consensus on Social Networks
On the Complexity of Nash Equilibria of Action-Graph Games
Linear Level Lasserre Lower Bounds for Certain k-CSPs
Tight Integrality Gaps for Lovasz-Schrijver LP Relaxations of Vertex Cover and Max Cut
A Linear Round Lower Bound for Lovasz-Schrijver SDP Relaxations of Vertex Cover
Chora: Expert-based Peer-to-peer web search
The computational Complexity of Concisely Represented Games
GrowRange: Anytime VCG-Based Mechanisms
Fall 2023
SI: 670: Applied Machine Learning
Winter 2023
EECS 547 / SI: 652: Incentives and Strategic Behavior in Computational Systems
SI 602: Mathematical Foundations for Data Science
Fall 2022
SI: 670: Applied Machine Learning
Winter 2021
SIADS 502: Math Methods for Data Science
SIADS 521: Visual Exploration of Data
Fall 2020
EECS 547 / SI: 652: Incentives and Strategic Behavior in Computational Systems
SI: 670: Applied Machine Learning
SIADS 502: Math Methods for Data Science
Winter 2020
SIADS 502: Math Methods for Data Science
SIADS 521: Visual Exploration of Data
Fall 2019
EECS 547 / SI: 652: Electronic Commerce (about algorithmic game theory)
SI: 670: Applied Machine Learning
Fall 2017
EECS 547 / SI: 652: Electronic Commerce (about algorithmic game theory) .
Winter 2017
EECS 376 Foundations of Computing
Fall 2015
EECS 598-06 Randomness and Computation
Winter 2015
EECS 376 Foundations of Computing
Fall 2014
EECS 574 Computational Complexity Theory
Fall 2013
EECS 574 Computational Complexity Theory
Winter 2013
EECS 203 Discrete Math
Fall 2012
EECS 598-06 Social Networks: Reasoning about Structure and Processes
This class looked at social networks research and how a theoretical computer science prospective both brings new questions and gains additional insights into this growing body of research. Schedule and readings on the website.
New Jersey Governor's School: The Math Behind the Maching, Summer 2012
New Jersey Governor's School: The Math Behind the Maching, Summer 2011
3341 North Quad
I was born in Green Bay, WI and moved to Wichita,
KS when I was nine. I attended Harvard University, graduating with highest honors in mathematics.
Afterwards, I attended Oxford University as the von Clemm fellow and
studied theology. I received my PhD from UC Berkeley in computer science where I
was advised by Luca Trevisan. Subsequently I was the Simons Foundation Postdoctoral Research Fellow in Theoretical
Computer Science at Princeton University.
G. Schoenebeck, C. Yu, F. Yu
G. Schoenebeck, F. Yu.
ITCS 2021, Arxiv
N. Burrell, G. Schoenebeck
ITCS 2021, Arxiv
G. Schoenebeck, F. Yu
EC' 2020, pdf
G. Schoenebeck, B. Tao, F. Yu
AAMAS '20, arXiv
Y. Kong, G. Schoenebeck, B. Tao, F. Yu
AAAI '20, pdf
G. Schoenebeck, B. Tao
EC '19,
Video Presentation,
TEAC '20
Y. Kong, C. Peikert, G. Schoenebeck, B. Tao
Wine '19, arXiv
G. Schoenebeck, B. Tao, F. Yu
Approx/Random '19, arXiv
Y. Kong, G. Schoenebeck.
TEAC '19,
Arxiv
J. Gao, G. Ghsemisefeh and J. Jones, G. Schoenebeck.
SocArXiv '19.
G. Schoenebeck, B. Tao
ToCT '19,
Wine '17,
arXiv '17
G. Schoenebeck, B. Tao, F. Yu
Approx/Random '19
J. Gao, G. Schoenebeck, F. Yu
AAMAS '19,
pdf
Y. Kong, C. Peikert, G. Schoenebeck, B. Tao,
Wine'19, arXiv
S. Su, V. G. Subramanian, G. Schoenebeck
NetEcon '19, arXiv
Y. Kong, G. Schoenebeck
Y. Kong, G. Schoenebeck
X. Ma, B. Li, Y. Wang, S. M. Erfani, S. Wijewickrema, M. E. Houle, G. Schoenebeck, D. Song, J. Bailey
ICLR '18, arXiv '18
G. Schoenebeck, F. Yu
SODA '18, pdf
Y. Kong, G. Schoenebeck.
ITCS '18
Y. Kong, G. Schoenebeck..
ITCS' 18, Arxiv '16
B. Tian, J. Huang, B. Mozafari, G. Schoenebeck
VLDB'18
R. Angell, G. Schoenebeck
WINE'17, arXiv '16
J. Gao, G. Schoenebeck, F. Yu
WINE '17
J. Huang, B. Mozafari, G. Schoenebeck, T. Wenisch
SIGMOD '17
J. Gao, B. Li, G. Schoenebeck, F. Yu
AAAI '17
R.Ebrahimi, J. Gao, G. Ghasemiesfeh, G. Schoenebeck
IEEE Transactions on Network Science and Engineering '17, arXiv '14
A. Snook, G. Schoenebeck, F. Yu.
EC '16
J. Gao, G. Ghasemiesfeh, G. Schoenebeck, F. Yu
EC '16
G. Schoenebeck, F. Yu
WINE '16
Y. Kong, K. Ligett, G. Schoenebeck.
WINE '16, Arxiv '15
J. Huang, B. Mozafari, G. Schoenebeck, T. Wenisch
arXiv'16
Y. Kong, G. Schoenebeck..
Arxiv '15
R. Ebrahimi, J. Gao, G. Ghasemiesfeh, G. Schoenebeck
ITCS '15
A. Ghosh, K. Ligett, A. Roth, G. Schoenebeck.
EC '14
T. Martin, G. Schoenebeck, M. Wellman
EC '14
P. Codenotti, G. Schoenebeck, A. Snook
arXiv '14
S. Chechik, D. H. Larkin, L. Roditty, G. Schoenebeck, R. E. Tarjan, V. V. Williams
SODA '14
G. Schoenebeck
WWW '13
A. Roth, G. Schoenebeck.
EC '12
S. Arora, R. Ge, S. Sachdeva, G. Schoenebeck
EC '12
R. Frongillo, G. Schoenebeck, O. Tamuz
Wine '11
T. Hollenstein, G. Schoenebeck
TCC '11
A. Gupta, A. Roth. G. Schoenebeck, K. Talwar.
WINE '10
G. Schoenebeck
PhD Thesis, 2010
A. Bhattacharyya, S. Kopparty, G. Schoenebeck, M. Sudan, D. Zuckerman
FOCS '10.
S. Yardi, D. Romero, G. Schoenebeck. d. boyd.
First Monday '10
E. Mossel, G. Schoenebeck
ICS '10.
C. Daskalakis, G. Schoenebeck, G. Valiant, P. Valiant
Soda '09.
G. Schoenebeck.
FOCS '08
G. Schoenebeck, L. Trevisan, M. Tulsiani.
STOC '07
G. Schoenebeck, L. Trevisan, M. Tulsiani.
CCC '07
H. Gylfason, O. Khan, G. Schoenebeck
AP2PC workshop at AAMAS '06.
G. Schoenebeck, S. Vadhan.
EC '06. ACM Transactions on Computation Theory 2012.
D. Parkes, G. Schoenebeck.
AAAI '04.
Teaching:
Contact Information:
501 State St.
University of Michigan
Ann Arbor, MI 48109-2121
Phone: (734)647-4712
Email:
Personal: